class_docstr: Test the CVScores visualizer
docstr: Test that visualizer can handle sklearn pipeline as model input
docstr_tok: ["Test","that","visualizer","can","handle","sklearn","pipeline","as","model","input"]
code: def test_pipeline_as_model_input(self):
      """
      Test that visualizer can handle sklearn pipeline as model input
      """
      X, y = load_mushroom(return_dataset=True).to_numpy()
      X = OneHotEncoder().fit_transform(X).toarray()
  
      cv = StratifiedKFold(n_splits=2, shuffle=True, random_state=11)
      model = Pipeline(
          [("minmax", MinMaxScaler()), ("nb", BernoulliNB())]
      )
  
      oz = CVScores(model, cv=cv)
      oz.fit(X, y)
      oz.finalize()
      self.assert_images_similar(oz, tol=2.0)
  
imports: import pytest
  import numpy.testing
  from unittest.mock import patch
  from tests.base import VisualTestCase
  from sklearn.svm import SVC
  from sklearn.naive_bayes import BernoulliNB
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.linear_model import RidgeCV, LogisticRegressionCV
  from sklearn.model_selection import ShuffleSplit, StratifiedKFold
  from sklearn.pipeline import Pipeline
  from yellowbrick.datasets import load_mushroom
  from yellowbrick.model_selection.cross_validation import *
label_desc: Use image node_id 11 for calling the TestCrossValidation obj's underlying member method code with example usage: obj.test_pipeline_as_model_input() without return types